Graph neural networks (GNNs) have shown great potential in representation learning for various graph tasks. However, the distribution shift between the training and test sets poses a challenge to the efficiency of GNNs. To address this challenge, HomoTTT propose a fully test-time training (FTTT) framework for GNNs to enhance the model’s generalization capabilities for node classification tasks. Specifically, our proposed HomoTTT designs a homophily-based and parameter-free graph contrastive learning task with adaptive augmentation to guide the model’s adaptation during the test time training, allowing the model to adapt for specific target data. In the inference stage, HomoTTT proposes to integrate the original GNN model and the adapted model after TTT using a homophily-based model selection method, which prevents potential performance degradation caused by unconstrained model adaptation. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of our proposed framework. Additionally, the exploratory study further validates the rationality of the homophily-based graph contrastive learning task with adaptive augmentation and the homophily-based model selection designed in HomoTTT.

图神经网络（GNN）在各种图任务的表示学习中表现出了巨大的潜力。然而，训练集和测试集之间的分布变化对 GNN 的效率提出了挑战。为了应对这一挑战，HomoTTT提出了 GNN 的完全测试时训练（FTTT）框架，以增强模型对节点分类任务的泛化能力。具体来说，我们提出的HomoTTT设计了一个基于同质性和无参数的图对比学习任务，具有自适应增强功能，以指导模型在测试时间训练期间的适应，从而使模型能够适应特定的目标数据。在推理阶段，HomoTTT提出使用基于同质性的模型选择方法来集成原始 GNN 模型和 TTT 后的适应模型，这可以防止由于无约束的模型适应而导致的潜在性能下降。六个基准数据集的广泛实验结果证明了我们提出的框架的有效性。此外，探索性研究进一步验证了基于同质性的自适应增强图对比学习任务以及HomoTTT中设计的基于同质性的模型选择的合理性。